Knowledge Hut

JANUARY 18, 2024

It entails using various technologies, including data mining, data transformation, and data cleansing, to examine and analyze that data. Both data science and software engineering rely largely on programming skills. However, data scientists are primarily concerned with working with massive datasets.

Software Engineering 97

Software Engineering Software Engineer Data Science Engineering 97

Knowledge Hut

SEPTEMBER 27, 2023

In an era dominated by data, organizations are in constant pursuit of tools that can transform raw information into actionable insights. This quest has led to the prominence of Power BI , a dynamic business intelligence platform developed by Microsoft. Use metrics or quantifiable achievements to showcase your contributions.

BI 52

BI Business Intelligence Raw Data Data Cleanse 52

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. In addition to this, they make sure that the data is always readily accessible to consumers.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

Knowledge Hut

MAY 3, 2024

Spark Streaming Kafka Streams 1 Data received from live input data streams is Divided into Micro-batched for processing. processes per data stream(real real-time) 2 A separate processing Cluster is required No separate processing cluster is required. it's better for functions like row parsing, data cleansing, etc.

Kafka 98

Kafka Scala Java Amazon Web Services 98

U-Next

OCTOBER 25, 2022

Digital content theft, fraudulent data manipulation, and sabotage are among the most prevalent forms of cybercrime. The attackers falsify the data analysis of operators with targeted bot requests, causing them to make decisions that are not based on reality. . Tableau: Tableau is a Salesforce tool used for data manipulation.

Business Intelligence 40

Business Intelligence Raw Data Data Cleanse Data Analysis 40

ProjectPro

JANUARY 27, 2023

Top ETL Business Use Cases for Streamlining Data Management Data Quality - ETL tools can be used for data cleansing, validation, enriching, and standardization before loading the data into a destination like a data lake or data warehouse.

BI 52

BI ETL Tools Retail Healthcare 52

Knowledge Hut

JANUARY 30, 2024

Whether it's aggregating customer interactions, analyzing historical sales trends, or processing real-time sensor data, data extraction initiates the process. Utilizes structured data or datasets that may have already undergone extraction and preparation. Primary Focus Structuring and preparing data for further analysis.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

AltexSoft

MAY 14, 2021

With the ETL approach, data transformation happens before it gets to a target repository like a data warehouse, whereas ELT makes it possible to transform data after it’s loaded into a target system. Data storage and processing. Data cleansing. Before getting thoroughly analyzed, data ? Data analysis.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

AUGUST 24, 2021

To understand their requirements, it is critical to possess a few basic data analytics skills to summarize the data better. So, add a few beginner-level data analytics projects to your resume to highlight your Exploratory Data Analysis skills. Blob Storage for intermediate storage of generated predictions.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

U-Next

SEPTEMBER 13, 2022

A multidisciplinary field called Data Science involves unprocessed data mining, its analysis, and discovering patterns utilized to extract meaningful information. The fundamental building blocks of Data Science are Statistics, Machine Learning, Computer Science, Data Analysis, Deep Learning, and Data Visualization. .

Machine Learning 40

Machine Learning Data Science Deep Learning Algorithm 40

ProjectPro

DECEMBER 7, 2021

4) Data Visualization The data analysis process includes more than just extracting useful insights from data. Another important aspect is to use those insights to construct a narrative that will help you make better business decisions. 2) What aspect of data intrigues you the most?

Portfolio 52

Portfolio Building Data Mining Data Analysis 52

Edureka

APRIL 17, 2023

For this project, you can start with a messy dataset and use tools like Excel, Python, or OpenRefine to clean and pre-process the data. You’ll learn how to use techniques like data wrangling, data cleansing, and data transformation to prepare the data for analysis.

Portfolio 40

Portfolio Data Analytics Project Data Mining 40

Edureka

FEBRUARY 8, 2024

Batch Processing- C-Series instances excel in scenarios that involve batch processing, where large amounts of data need to be processed in parallel. This is beneficial for tasks like data transformation, data cleansing, and data analysis.

AWS 52

AWS NoSQL Deep Learning Datasets 52

ProjectPro

JANUARY 31, 2023

To manage these large amounts of data, testing necessitates using specific tools, frameworks, and processes. Big data analysis refers to the generation of data and its storage, retrieval of data, and analysis of large data in terms of volume and speed variation. What is the goal of A/B testing?

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

OCTOBER 20, 2021

Most Data Scientists know how to run python code on a Jupyter Notebook. We run the codes, do data analysis, come up with the final model result and stop there. Data Volumes and Veracity Data volume and quality decide how fast the AI System is ready to scale. Data: Data Engineering Pipelines Data is everything.

Machine Learning 52

Machine Learning Algorithm Data Science Government 52